Recommending MeSH terms for annotating biomedical articles

Abstract

BackgroundDue to the high cost of manual curation of key aspects from the scientific literature, automated methods for assisting this process are greatly desired. Here, we report a novel approach to facilitate MeSH indexing, a challenging task of assigning MeSH terms to MEDLINE citations for their archiving and retrieval.MethodsUnlike previous methods for automatic MeSH term assignment, we reformulate the indexing task as a ranking problem such that relevant MeSH headings are ranked higher than those irrelevant ones. Specifically, for each document we retrieve 20 neighbor documents, obtain a list of MeSH main headings from neighbors, and rank the MeSH main headings using ListNet–a learning-to-rank algorithm. We trained our algorithm on 200 documents and tested on a previously used benchmark set of 200 documents and a larger dataset of 1000 documents.ResultsTested on the benchmark dataset, our method achieved a precision of 0.390, recall of 0.712, and mean average precision (MAP) of 0.626. In comparison to the state of the art, we observe statistically significant improvements as large as 39% in MAP (p-value <0.001). Similar significant improvements were also obtained on the larger document set.ConclusionExperimental results show that our approach makes the most accurate MeSH predictions to date, which suggests its great potential in making a practical impact on MeSH indexing. Furthermore, as discussed the proposed learning framework is robust and can be adapted to many other similar tasks beyond MeSH indexing in the biomedical domain. All data sets are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/indexing.

Improving image retrieval effectiveness via query expansion using MeSH hierarchical structure

“…Consequently, the user can search the structure from the more general to the more specific and vice versa. In Huang et al 21 the authors recommend the use of MeSH terms in order to annotate biomedical articles. In that work, the researchers offer new methods for indexing biomedical documents based on the MeSH terms found in them.…”

Indexation automatique de documents en santé : évaluation et analyse de sources d’erreurs

“…Les travaux sur le MeSH en anglais ont utilisé le modèle probabiliste [4] et les techniques d'apprentissage automatique telles que le réseau bayésien [5] et les k-plus proches voisins pour la classification des documents [2,[6][7][8]. Aronson et al [2] exploitent également l'outil MetaMap [9] et la méthode de tri-gram (cette méthode permet de déterminer la similarité entre deux phrases) pour l'extraction des concepts Unified Medical Language System (UMLS 1 ) qui sont ensuite restreints aux concepts MeSH.…”

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“…Similarly in [9] they report a novel approach to facilitate MeSH indexing, assigning MeSH terms to MEDLINE citations for archiving and retrieval purposes. For each document they retrieve k neighbour documents, they obtain a list of MeSH main headings from neighbours, and they rank the MeSH main headings using a learning-to-rank algorithm.…”